Distributed inferencing performance is critical for the broader adoption of AI applications in enterprises. To support this, SONiC must be equipped to handle the demands of AI scale-out environments. This talk outlines the key performance indicators (KPIs) necessary for SONiC readiness—specifically latency, throughput, and reliability. It also explores the role of disaggregated inferencing in current and future AI deployments, and how SONiC can be effectively utilized to support both prefill and decoding networks in distributed inference architectures.